Evaluation of Remote Sensing Aerial Systems in Existing Transportation Practices

The application of small Remotely-Controlled (R/C) aircraft for aerial photography presents many unique advantages over manned aircraft due to their lower acquisition cost, lower maintenance issue, and superior flexibility. The extraction of reliable information from these images could benefit DOT engineers in a variety of research topics including, but not limited to, work zone management, traffic congestion, safety, and environmental. During this effort, one of the West Virginia University R/C aircraft, named 'Foamy', has been instrumented for a proof-of-concept demonstration of aerial data acquisition. Specifically, the aircraft has been outfitted with a GPS receiver, a flight data recorder, a downlink telemetry hardware, a digital still camera, and a shutter-triggering device. During the flight a ground pilot uses one of the R/C channels to remotely trigger the camera. Several hundred high-resolution geo-tagged aerial photographs were collected during 10 flight experiments at two different flight fields. A Matlab based geo-reference was developed for measuring distances from an aerial image and estimating the geo-location of each ground asset of interest. A comprehensive study of potential Sources of Errors (SOE) has also been performed with the goal of identifying and addressing various factors that might affect the position estimation accuracy. The result of the SOE study concludes that a significant amount of position estimation error was introduced by either mismatching of different measurements or by the quality of the measurements themselves. The first issue is partially addressed through the design of a customized Time-Synchronization Board (TSB) based on a MOD 5213 embedded microprocessor. The TSB actively controls the timing of the image acquisition process, ensuring an accurate matching of the GPS measurement and the image acquisition time. The second issue is solved through the development of a novel GPS/INS (Inertial Navigation System) based on a 9-state Extended Kalman Filter. The developed sensor fusion algorithm provides a good estimation of aircraft attitude angle without the need for using expensive sensors. Through the help of INS integration, it also provides a very smooth position estimation that eliminates large jumps typically seen in the raw GPS measurements.

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  • Corporate Authors:

    West Virginia University, Morgantown

    74 Woodland Terrace
    Morgantown, WV  United States  26505

    Research and Innovative Technology Administration

    Office of University Programs, 1200 New Jersey Avenue, SE
    Washington, DC  United States  20590

    West Virginia Department of Highways

    1900 Kanawha Boulevard East
    Charleston, WV  United States  25305
  • Authors:
    • Gu, Yu
  • Publication Date: 2009-10


  • English

Media Info

  • Media Type: Print
  • Edition: Final Report
  • Features: Figures; References; Tables;
  • Pagination: 41p

Subject/Index Terms

Filing Info

  • Accession Number: 01144495
  • Record Type: Publication
  • Report/Paper Numbers: WVU-2008-01
  • Contract Numbers: DTRT07-G-0003 (Grant)
  • Created Date: Nov 16 2009 1:06PM